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Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors.

Xiang Ji1, Benjamin Redelings1, Shuo Su2

  • 1Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA, USA.

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|July 17, 2025
PubMed
Summary

This study introduces a new computational method to automatically detect evolutionary change-points in DNA sequences. The approach improves efficiency for analyzing genetic data, such as in BRCA1 gene evolution and mpox virus mutations.

Keywords:
Bayesian inferencebranch-specific substitution modellinear-time gradient algorithmmaximum likelihoodnatural selection

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genomics

Background:

  • Branch-specific substitution models are key for identifying evolutionary shifts.
  • Current methods require predefined change-point locations or struggle with large datasets.

Purpose of the Study:

  • To develop a method for automatic change-point detection without prior knowledge.
  • To enhance the scalability and efficiency of evolutionary model inference.

Main Methods:

  • Integration of branch-specific substitution models with shrinkage priors.
  • Development of an analytical gradient algorithm for high-dimensional parameter estimation.
  • Application to primate BRCA1 gene evolution and mpox viral sequences.

Main Results:

  • Automatic identification of evolutionary change-points.
  • Significant computational speedups: up to 90x in maximum-likelihood optimization and 360x in Bayesian inference.
  • Efficient estimation of distinct substitution parameters per branch.

Conclusions:

  • The novel algorithm overcomes limitations of existing methods for detecting evolutionary change-points.
  • Enhanced inference efficiency and computational performance for complex evolutionary analyses.
  • Provides a powerful tool for studying selection pressure and mutational dynamics in diverse organisms.